Disentangling the cognitive components supporting Austin Maze performance in left versus right temporal lobe epilepsy

Neuropsychological tests requiring patients to find a path through a maze can be used to assess visuospatial memory performance in temporal lobe pathology, particularly in the hippocampus. Alternatively, they have been used as a task sensitive to executive function in patients with frontal lobe damage. We measured performance on the Austin Maze in patients with unilateral left and right temporal lobe epilepsy (TLE), with and without hippocampal sclerosis, compared to healthy controls. Performance was correlated with a number of other neuropsychological tests to identify the cognitive components that may be associated with poor Austin Maze performance. Patients with right TLE were significantly impaired on the Austin Maze task relative to patients with left TLE and controls, and error scores correlated with their performance on the Block Design task. The performance of patients with left TLE was also impaired relative to controls; however, errors correlated with performance on tests of executive function and delayed recall. The presence of hippocampal sclerosis did not have an impact on maze performance. A discriminant function analysis indicated that the Austin Maze alone correctly classified 73.5% of patients as having right TLE. In summary, impaired performance on the Austin Maze task is more suggestive of right than left TLE; however, impaired performance on this visuospatial task does not necessarily involve the hippocampus. The relationship of the Austin Maze task with other neuropsychological tests suggests that differential cognitive components may underlie performance decrements in right versus left TLE.

Can larger cemented femoral components reduce periprosthetic fractures?

Introduction The risk for late periprosthetic fractures is higher in patients treated for a neck of femur fracture compared to those treated for osteoarthritis. It has been hypothesised that osteopenia and consequent decreased stiffness of the proximal femur are responsible for this. We investigated if a femoral component with a bigger body would increase the torque to failure in a biaxially loaded composite sawbone model. Method A biomechanical composite sawbone model was used. Two different body sizes (Exeter 44-1 vs 44-4) of a polished tapered cemented stem were implanted by an experienced surgeon, in 7 sawbones each and loaded at 40 deg/s internal rotation until failure. Torque to fracture and fracture energy were measured using a biaxial materials testing device (Instron 8874). Data are non-parametric and tested with Mann-Whitney U-test. Results The mean torque load to fracture was 154.1 NM (SD 4.4) for the 44-1 stem and 229 NM (SD10.9) for the 44-4 stem (p = 0.01). The mean fracture energy was 9.6 J (SD1.2) for the 44-1 stem and 17.2 J (SD2.0) for the 44-4 stem (p = 0.14). Conclusion the use of a large body polished tapered cemented stem for neck of femur fractures increases the torque to failure in a biomechanical model and therefore is likely to reduce late periprosthetic fracture risk in this vulnerable cohort.

Interactions of ingested food, beverage, and tobacco components involving human cytochrome P4501A2, 2A6, 2E1, and 3A4 enzymes

Human cytochrome P450 (P450) enzymes are involved in the oxidation of natural products found in foods, beverages, and tobacco products and their catalytic activities can also be modulated by components of the materials. The microsomal activation of aflatoxin B1 to the exo-3,9-epoxide is stimulated by flavone and 7,8-benzoflavone, and attenuated by the flavonoid naringenin, a major component of grapefruit. P4502E1 has been demonstrated to play a potentially major role in the activation of a number of very low-molecular weight cancer suspects, including ethyl carbamate (urethan), which is present in alcoholic beverages and particularly stone brandies. The enzyme (P4502E1) is also known to be inducible by ethanol. Tobacco contains a large number of potential carcinogens. In human liver microsomes a significant role for P4501A2 can be demonstrated in the activation of cigarette smoke condensate. Some of the genotoxicity may be due to arylamines. P4501A2 is also inhibited by components of crude cigarette smoke condensate. The tobacco-specific nitrosamines are activated by a number of P450 enzymes. Of those known to be present in human liver, P4501A2, 2A6, and 2E1 can activate these nitrosamines to genotoxic products.

Enhancement of the chemoprotective enzymes glucuronosyl transferase and glutathione transferase in specific organs of the rat by the coffee components kahweol and cafestol

The coffee components kahweol and cafestol (K/C) have been reported to protect the colon and other organs of the rat against the formation of DNA adducts by 2-amino-1-methyl-6-phenylimidazo[4,5-b] pyridine (PhIP) and aflatoxin B1. PhIP is a cooked-food mutagen to which significant human exposure and a role in colon cancer etiology are attributed, and, interestingly, such cancers appear to develop at a lower rate in consumers of coffees with high amounts of K/C. Earlier studies in rodent liver have shown that a key role in the chemopreventive effect of K/C is likely to be due to the potential of these compounds to induce the detoxification of xenobiotics by glutathione transferase (GST) and to enhance the synthesis of the corresponding co-factor glutathione. However, mutagens like PhIP may also be detoxified by UDP-glucuronosyl transferase (UDPGT) for which data are lacking regarding a potential effect of K/C. Therefore, in the present study, we investigated the effect of K/C on UDPGT and, concomitantly, we studied overall GST and the pattern of individual GST classes, particularly GST-θ, which was not included in earlier experiments. In addition, we analyzed the organ-dependence of these potentially chemopreventive effects. K/C was fed to male F344 rats at 0.122% in the chow for 10 days. Enzyme activities in liver, kidney, lung, colon, salivary gland, pancreas, testis, heart and spleen were quantified using five characteristic substrates and the hepatic protein pattern of GST classes α, μ, and π was studied with affnity chromatography/HPLC. Our study showed that K/C is not only capable of increasing overall GST and GST classes α, μ, and π but also of enhancing UDGPT and GST-θ. All investigated K/C effects were strongest in liver and kidney, and some response was seen in lung and colon but none in the other organs. In summary, our results show that K/C treatment leads to a wide spectrum of increases in phase II detoxification enzymes. Notably, these effects occurred preferentially in the well perfused organs liver and kidney, which may thus not only contribute to local protection but also to anti-carcinogenesis in distant, less stimulated organs such as the colon.

Near infrared spectroscopy for rapid determination of Mankin score components : a potential tool for quantitative characterization of articular cartilage at surgery

PURPOSE The purpose of this study was to demonstrate the potential of near infrared (NIR) spectroscopy for characterizing the health and degenerative state of articular cartilage based on the components of the Mankin score. METHODS Three models of osteoarthritic degeneration induced in laboratory rats by anterior cruciate ligament (ACL) transection, meniscectomy (MSX), and intra-articular injection of monoiodoacetate (1 mg) (MIA) were used in this study. Degeneration was induced in the right knee joint; each model group consisted of 12 rats (N = 36). After 8 weeks, the animals were euthanized and knee joints were collected. A custom-made diffuse reflectance NIR probe of 5-mm diameter was placed on the tibial and femoral surfaces, and spectral data were acquired from each specimen in the wave number range of 4,000 to 12,500 cm(-1). After spectral data acquisition, the specimens were fixed and safranin O staining (SOS) was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis, with spectral preprocessing and wavelength selection technique, the spectral data were then correlated to the structural integrity (SI), cellularity (CEL), and matrix staining (SOS) components of the Mankin score for all the samples tested. RESULTS ACL models showed mild cartilage degeneration, MSX models had moderate degeneration, and MIA models showed severe cartilage degenerative changes both morphologically and histologically. Our results reveal significant linear correlations between the NIR absorption spectra and SI (R(2) = 94.78%), CEL (R(2) = 88.03%), and SOS (R(2) = 96.39%) parameters of all samples in the models. In addition, clustering of the samples according to their level of degeneration, with respect to the Mankin components, was also observed. CONCLUSIONS NIR spectroscopic probing of articular cartilage can potentially provide critical information about the health of articular cartilage matrix in early and advanced stages of osteoarthritis (OA). CLINICAL RELEVANCE This rapid nondestructive method can facilitate clinical appraisal of articular cartilage integrity during arthroscopic surgery.

CORR Insights® : loss of cement-bone interlock in retrieved tibial components from total knee arthroplasties

Most surgeons cement the tibial component in total knee replacement surgery. Mid-term registry data from a number of countries, including those from the United Kingdom and Australia, support the excellent survivorship of cemented tibial components. In spite of this success, results can always be improved, and cementing technique can play a role. Cementing technique on the tibia is not standardized, and surgeons still differ about the best ways to deliver cement into the cancellous bone of the upper tibia. Questions remain regarding whether to use a gun or a syringe to inject the cement into the cancellous bone of the tibial plateau . The ideal cement penetration into the tibial plateau is debated, though most reports suggest that 4 mm to 10 mm is ideal. Thicker mantles are thought to be dangerous due to the risk of bone necrosis, but there is little in the literature to support this contention...

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

The contribution of three components of nutrition knowledge to socio-economic differences in food purchasing choices

Objectives: To assess socio-economic differences in three components of nutrition knowledge, i.e. knowledge of (i) the relationship between diet and disease, (ii) the nutrient content of foods and (iii) dietary guideline recommendations; furthermore, to determine if socio-economic differences in nutrition knowledge contribute to inequalities in food purchasing choices. Design: The cross-sectional study considered household food purchasing,nutrition knowledge, socio-economic and demographic information. Household food purchasing choices were summarised by three indices, based on self-reported purchasing of sixteen groceries, nineteen fruits and twenty-one vegetables. Socio-economic position (SEP) was measured by household income and education. Associations between SEP, nutrition knowledge and food purchasing were examined using general linear models adjusted for age, gender, household type and household size. Setting: Brisbane, Australia in 2000. Subjects: Main household food shoppers (n 1003, response rate 66?4 %), located in fifty small areas (Census Collectors Districts). Results: Shoppers in households of low SEP made food purchasing choices that were less consistent with dietary guideline recommendations: they were more likely to purchase grocery foods comparatively higher in salt, sugar and fat, and lower in fibre, and they purchased a narrower range of fruits and vegetables. Those of higher SEP had greater nutrition knowledge and this factor attenuated most associations between SEP and food purchasing choices. Among nutrition knowledge factors, knowledge of the relationship between diet and disease made the greatest and most consistent contribution to explaining socio-economic differences in food purchasing. Conclusions: Addressing inequalities in nutrition knowledge is likely to reduce socio-economic differences in compliance with dietary guidelines. Improving knowledge of the relationship between diet and disease appears to be a particularly relevant focus for health promotion aimed to reduce socio-economic differences in diet and related health inequalities.